{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:45:44Z","timestamp":1776811544101,"version":"3.51.2"},"reference-count":22,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2022,9,5]]},"abstract":"<jats:p>The evaluation of new power system construction is the research foundation for improving the flexible regulation ability and comprehensive operational efficiency of new power systems, and achieve the comprehensive goals of safe power supply, green consumption, and economic efficiency. However, the existing research on the evaluation index system of new power system construction can not fully reflect the main objectives of new power system construction. Therefore, this paper first develops a source-load and green-intelligence multi-level and multi-dimensional evaluation system for new power system construction from source-load side equipment, green power, reliable power supply, and intelligent power consumption. Secondly, a hybrid optimization algorithm is proposed based on fluid search algorithm (FSO) for improving the Long Short-Term Memory (LSTM) neural network parameter updating method. Then, the improved LSTM neural network is applied to the construction evaluation of the new power system. Finally, the simulation results show that the evaluation error of the new power system construction evaluation method is 0.0063, which has a high evaluation.<\/jats:p>","DOI":"10.3233\/jcm-226445","type":"journal-article","created":{"date-parts":[[2022,8,23]],"date-time":"2022-08-23T11:41:47Z","timestamp":1661254907000},"page":"1819-1832","source":"Crossref","is-referenced-by-count":4,"title":["Evaluation method of a new power system construction based on improved LSTM neural network"],"prefix":"10.66113","volume":"22","author":[{"given":"Weiguo","family":"Si","sequence":"first","affiliation":[{"name":"State Grid Chongqing Electric Power Company, Chongqing, China"}]},{"given":"Weifang","family":"Lin","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute, Beijing, China"}]},{"given":"Daolin","family":"Xu","sequence":"additional","affiliation":[{"name":"State Grid Chongqing Electric Power Company, Chongqing, China"}]},{"given":"Yuanbo","family":"Luo","sequence":"additional","affiliation":[{"name":"State Grid Chongqing Electric Power Company, Chongqing, China"}]},{"given":"Ninghui","family":"Han","sequence":"additional","affiliation":[{"name":"China Electric Power Research Institute, Beijing, China"}]}],"member":"55691","reference":[{"issue":"11","key":"10.3233\/JCM-226445_ref1","first-page":"102","article-title":"Robust reactive power partitioning method for frequent power flow fluctuation in new power system","volume":"46","author":"Li","year":"2022","journal-title":"Auto Electr Power Syst."},{"issue":"3","key":"10.3233\/JCM-226445_ref2","first-page":"821","article-title":"Key scientific problems and research framework for carbon perspective research of new power systems","volume":"46","author":"Kang","year":"2022","journal-title":"Power Syst Technol."},{"key":"10.3233\/JCM-226445_ref3","unstructured":"Zhang ZG, Kang CQ. Challenges and prospects for constructing the new-type power system towards a carbon neutrality future. 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Fuzzy synthetic evaluation method of cascading failure sequence in power grid. Proc CSU-EPSA. 2015; 27(4): 79-85."},{"issue":"1","key":"10.3233\/JCM-226445_ref17","first-page":"94","article-title":"Ambient data-driven on-line evaluation method of power system small signal stability","volume":"46","author":"Zhou","year":"2022","journal-title":"Auto Electr Power Syst."},{"key":"10.3233\/JCM-226445_ref18","unstructured":"Li FC, Xu J, Liao SY, Sun YZ, Ke DP, Yang J, Du JM. Multi-stage power system transient stability assessment based on sample attention and hierarchical features. 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